soyunchoi

segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

Grounded-Segment-Anything

Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

Swin-Transformer

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

dinov2

PyTorch code and models for the DINOv2 self-supervised learning method.

mae

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

EVA

EVA Series: Visual Representation Fantasies from BAAI

Semantic-Segment-Anything

Automated dense category annotation engine that serves as the initial semantic labeling for the Segment Anything dataset (SA-1B).

Swin-Transformer-Object-Detection

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.

ViT-Adapter

[ICLR 2023 Spotlight] Vision Transformer Adapter for Dense Predictions

Awesome_Prompting_Papers_in_Computer_Vision

A curated list of prompt-based paper in computer vision and vision-language learning.

ov-seg

This is the official PyTorch implementation of the paper Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP.

OpenAI-CLIP

Simple implementation of OpenAI CLIP model in PyTorch.

vmoe

Awesome-Mixture-of-Experts-Papers

A curated reading list of research in Mixture-of-Experts(MoE).

model-soups

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

deephar

Deep human action recognition and pose estimation

ICRA2024PaperList

ICRA2024 Paper List

PointTransformerV2

[NeurIPS'22] An official PyTorch implementation of PTv2.

tvl

multimodal-material-segmentation

PGN

Prompt Generation Networks for Efficient Adaptation of Frozen Vision Transformers. Jochem Loedeman, Maarten C. Stol, Tengda Han, Yuki M. Asano. Tech Report. 2022

dap-cl

Official code of "Generating Instance-level Prompts for Rehearsal-free Continual Learning (ICCV 2023)"

GeminiFusion

DeformableObjectsGrasping

Touch-and-Go

supergb-d

mvitac

Self-Supervised Visual-Tactile Representation Learning via Multimodal Contrastive Training

ICL_multimodal

Code for paper 'Borrowing Treasures from Neighbors: In-Context Learning for Multimodal Learning with Missing Modalities and Data Scarcity'

NYUDepthV2_PointCloud_Converter

Utility to convert the NYU Depth V2 dataset into point clouds for advanced 3D visualization and analysis.

UCTNet